On the other hand, deep learning based reconstruction algorithms recently have led to state-of-the-art results in solving such ill-posed problems in computational imaging [1]}, [2]} [3]} [4]}. These approaches typically learn an inverse mapping from measurements to the signal by minimizing reconstruction loss on a set of training examples. However, this kind of training, popularly known as discriminative learning, makes the network task specific.
Furthermore, we need to retrain the network for various parameter settings of the forward model. For example, for every new setting of measurement rate and sensing matrix in SPC, we need to relearn the network parameters. Instead of having to design/retrain a different network for each task and parameter setting, it would be more efficient to have a generalized framework which can be used for solving various inverse problems.
